
import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute







def 							A__       (    lowerCamelCase = 3    )	->						qiskit.result.counts.Counts:
				if isinstance(lowerCamelCase  ,     lowerCamelCase    ):
								raise TypeError("""number of qubits must be a integer."""    )
				if number_of_qubits <= 0:
								raise ValueError("""number of qubits must be > 0."""    )
				if math.floor(lowerCamelCase    ) != number_of_qubits:
								raise ValueError("""number of qubits must be exact integer."""    )
				if number_of_qubits > 10:
								raise ValueError("""number of qubits too large to simulate(>10)."""    )

				UpperCamelCase_:				List[Any]						=    QuantumRegister(lowerCamelCase  ,     """qr"""    )
				UpperCamelCase_:				str						=    ClassicalRegister(lowerCamelCase  ,     """cr"""    )

				UpperCamelCase_:				int						=    QuantumCircuit(lowerCamelCase  ,     lowerCamelCase    )

				UpperCamelCase_:				Any						=    number_of_qubits

				for i in range(lowerCamelCase    ):
								quantum_circuit.h(number_of_qubits - i - 1    )
								counter -= 1
								for j in range(lowerCamelCase    ):
												quantum_circuit.cp(np.pi / 2 ** (counter - j)  ,     lowerCamelCase  ,     lowerCamelCase    )

				for k in range(number_of_qubits // 2    ):
								quantum_circuit.swap(lowerCamelCase  ,     number_of_qubits - k - 1    )

				# measure all the qubits
				quantum_circuit.measure(lowerCamelCase  ,     lowerCamelCase    )
				# simulate with 10000 shots
				UpperCamelCase_:				Any						=    Aer.get_backend("""qasm_simulator"""    )
				UpperCamelCase_:				Optional[int]						=    execute(lowerCamelCase  ,     lowerCamelCase  ,     shots=1_00_00    )

				return job.result().get_counts(lowerCamelCase    )


if __name__ == "__main__":
							print(
							    F"""Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}"""
							)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase_    :			Optional[int]    =	{
    """configuration_graphormer""": ["""GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GraphormerConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_    :			Union[str, Any]    =	[
							    """GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """GraphormerForGraphClassification""",
							    """GraphormerModel""",
							    """GraphormerPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_graphormer import (
														    GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GraphormerForGraphClassification,
														    GraphormerModel,
														    GraphormerPreTrainedModel,
														)


else:
							import sys

							lowerCamelCase_    :			Optional[Any]    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






